Characterizing topography of EDM generated surface by time series and autocorrelation function. (July 2017)

• Record Type: Journal Article
• Authors: Aich, Ushasta; Banerjee, Simul
• Is Part Of: Tribology international. Volume 111(2017)
• Page Start: 73
• Page End: 90
• Publication Date: 2017-07
• Subjects: Electrical discharge machining (EDM) -- Autocorrelation function (ACF) -- Periodicity to randomness ratio (PR ratio); Tribology -- Periodicals; 621.89
• DOI: 10.1016/j.triboint.2017.02.016
• Languages: English
• ISSNs: 0301-679X

Abstract

Abstract: Characterizing topography of machined surface is a way of getting insight into the machining phenomena. Here, a statistical procedure is proposed for extracting the topographical features of electric discharge machined (EDM) surface. Computed autocorrelation functions (ACF) of roughness profiles (considering as time series) exhibit possible random and periodic features buried on the machined surface. Through the decomposition of ACF curves and using backward linear prediction method, existing random and periodic patterns are separated. Very small values of a non-dimensional index - PR ratio indicate the presence of significant random variations in the machined surface. Spatial variations of characteristic lengths within a treatment are found as the most contributive part in overall variation and assert stochastic nature of surface development. Highlights: Roughness profiles are considered as time series and analyzed through ACF. Patterns of ACF curves suggest the existence of both random and periodic features. Parameters of the most effective constituent FIDs of ACF curves are estimated. Relative contribution of randomness and periodicity is assessed using PR ratio. Analysis of characteristic lengths claims in favor of randomness in surface profiles. Correlation between PR ratioavg with EDM process parameters is developed.